EDUCATION:
Graduate School: University of Virginia (Chemistry), Charlottesville, VA
Clinical Chemistry and Laboratory Medicine Fellowship: University of Virginia, Charlottesville, VA

CLINICAL:
Laboratory testing in Clinical Chemistry, Toxicology, Hemostasis, and Endocrinology.

RESEARCH:
Liquid chromatography mass spectrometry assay methods.

Dustin R. Bunch, is an Asst. Director of Clinical Chemistry & Co-Director Laboratory Informatics at Nationwide Children's Hospital. His research focuses small molecule analysis by mass spectrometry in a clinical setting and clinical informatics.

Working with R for Validation Summaries in the Clinical Laboratory
Lindsay Bazydlo

Putting R Skills to Use in Clinical Laboratories
Dustin Bunch

Paula M. Ladwig, M.S., MT (ASCP), is a Principal Developer with the Clinical Mass Spectrometry Development Laboratory, Department of Laboratory Medicine and Pathology at Mayo Clinic in Rochester, MN. She has over 15 years of experience in the development and validation of new mass spectrometry tests. Her interests include the implementation of therapeutic drug monitoring of monoclonal antibody therapeutics by mass spectrometry.

Session Overview

The clinical laboratory will have many roles as monoclonal antibody therapeutics (t-mAbs) expand: identifying potential interferences in routine immunoassays; developing new assays to differentiate a t-mAb from an endogenous, disease-causing plasma cell clone and monitoring therapeutic drugs for better patient outcomes and assessing loss of response to therapy. This session will provide an overview of mass spec techniques available to the clinical laboratory for t-mAb detection and quantitation along with their advantages and disadvantages. Finally this session will offer a few examples of hurdles in the implementation into the clinical laboratory setting.

Target Audience

This session is intended for clinical laboratory directors and pathologists, clinical technologists, IVD manufacturers, pharmaceutical scientists, and anyone interested in the mass spectrometry applications for therapeutic monoclonal antibodies especially those involved in development of new methods for t-mAb monitoring.

Needs Assessment

There are over 60 different therapeutic monoclonal antibodies (t-mAbs) approved by the FDA; used to treat a variety of diseases. The market for monoclonal antibodies is rapidly growing, with over 500 new t-mAbs in several stages of development. Laboratorians are quite familiar with the detection, screening, and quantitative therapeutic drug monitoring for small molecules as the methodologies have been well established, therapeutic ranges for many drugs have been defined, and the metabolic pathways for many small molecules have been elucidated in detail. However, this is not the case for the t-mAbs. The detection, screening, and quantitative measurement of these monoclonal antibodies require different technologies.

Mass spectrometry is an important tool in the field of t-mAbs; mostly because it is relatively simple and so versatile once you understand how to apply the basic principles, challenges and limitations of each different approach and instrumentation. This session will provide examples of approaches for mass spectrometry assay development for chimeric, humanized and fully human t-mAbs quantitation; from peptide by quadrupole MS to intact or subunit detection by time-of-flight or orbitrap MS.

Following the completion of this session, the participant will be able to:

1. Describe mass spectrometry techniques available for the assessment of monoclonal antibody therapeutics along with their advantages and disadvantages.

2. Discuss screening and quantitation approaches.

3. Describe common hurdles and how to avoid them.

Dr. Joe El-Khoury is Associate Professor of Laboratory Medicine at Yale School of Medicine, Director of the Clinical Chemistry Lab and Fellowship program at Yale-New Haven Health. He is board certified by the American Board of Clinical Chemistry (ABCC) and a fellow of the AACC Academy. He currently serves on the Board of Directors of AACC, as well as Chair of the Committee on Kidney Diseases for IFCC. His research interests are pre-analytical errors, biomarkers of kidney disease, and liquid chromatography-mass spectrometry in the clinical laboratory.

This informal Practical Training session will include a presentation on Financial Considerations for Purchasing a Mass Spectrometer, as well as offer small group discussion and networking opportunities.

1. Describe the clinical and monetary benefits of purchasing a mass spectrometry system.
2. Identify different models for financing a mass spectrometry system purchase.
3. Participate in effective negotiations with vendors.

B.S. in Analytical Chemistry, Masaryk University, Brno, Czech Republic.
Ph.D. in Bioanalytical Chemistry, Brigham Young University, Provo, Utah.
Nearly three decades of HPLC, CE, CE-MS, and LC-MSMS method development and validation experience in academic, pharmaceutical, and clinical laboratory environments.

Introduction
The popularity of LC-MS/MS-based methods for clinical testing continues to rise. However, despite their superior analytical specificity, these methods may still suffer from interference affecting method accuracy and precision, and hence negatively impacting patient care.
The aim of this Practical Training Track session is to introduce the participant to the following:

Segment #1
•Sources of guidelines for interference testing in method development/validation and routine testing (CLSI, CAP, SWGTOX)
•What is analytical interference and where does it come from?
•How do we define acceptable interference levels?
-When defining acceptable interference levels, consider the clinical context in which a test result will be used as well as the allowable analytical error limits.

Segment #2
•How do we test for interference in LC-MS/MS?
-Testing for specific interference (known drug, medication, supplement, sample abnormality, etc., added to the sample) vs. testing for unidentified interference (interference that cannot be anticipated or identified beforehand).
•When do we test for interference?
-Preferably, interference testing should be an integral part the method development process. Waiting until method validation to perform these experiments may result in unwanted surprises. Labs should use as many patient specimens as practical to ensure capturing the biological variability of interference.

Segment #3
•The use of internal standard in mitigating interference
•How do we monitor for interference?
-Even the best method development strategies rarely are able to prevent interference completely. Hence, the need to monitor for interference in routine testing to avoid reporting compromised results is undisputable. The most relevant data quality metrics are ion ratios, absolute internal standard areas, and retention times. Deviations in these metrics can signal the presence of interference in either the analyte or internal standard mass chromatograms.
Examples of interference issues in various methods and how they were resolved will be shown throughout the entire session.

Acknowledgements:
Many thanks as well to Donald Mason, Lisa Calton, and Stephen Balloch for their contributions as coauthors of the Clinical Laboratory News article “Interference Testing and Mitigation in LC-MS/MS Assays,” used in preparing this presentation. This work was supported in part by ARUP Institute for Clinical and Experimental Pathology®.

After each respective segment, attendees should be able to:

Segment #1
1.List sources of guidelines for interference testing
2.Define analytical interference and identify its various sources

Segment #2
1.Describe the different types of experiments used for interference testing in LC-MS/MS
2.Explain why waiting until validation to test for interference is not be desirable

Segment #3
1.Discuss the role of internal standard in mitigating interference
2.Name parameters used for interference monitoring

Slide Deck (PDF)

I graduated with a PhD from Dalhousie University in Halifax, NS, Canada in 2014 where I worked on developing and applying quantitative proteomics workflows for biomarker discovery. I then completed a Clinical Biochemistry Fellowship in Calgary, AB, Canada in 2016 before moving on to work as a Clinical Biochemist in the Fraser Health region in British Columbia, Canada. During this time I gained significant experience in using R and RStudio, writing numerous shiny apps to perform QC management and to streamline LC-MS/MS data workflows. In 2019, I moved to my current position in Calgary where I head the Mass Spectrometry testing facility for Alberta Precision Laboratories. Primarily focussed on toxicology testing, I have overseen a transition towards more endocrine testing and eliminated low-throughput GC-MS workflows in favour of targeted, MRM based LC-MS analyses. My research is focused on promoting LC-MS technologies and development of tools and workflows to bring targeted proteomics methodologies to routine clinical application.

Introduction: Review of quality control (QC) data in the clinical lab generally utilizes vendor-specific and costly middleware systems which may not be user friendly or display all desired information. Additionally, many instruments may not come with a middleware option, the middleware may be cost-prohibitive, or it may not allow off-site data review, making routine QC review fairly labour intensive or time-consuming. These issues are especially problematic in regions with de-centralized clinical testing networks or with multiple analyzer vendors, and review of QC data is often limited to rudimentary Laboratory Information System (LIS) functionality, which is generally not user friendly or intuitive to use.

Objectives: Design a user interface to allow streamlined QC data review and allow rapid multi-site and/or multi-analyzer QC comparisons using R.

Methods: This script employs R (version 3.6.1) and RStudio (version 1.2.1335) with packages shinydashboard, ggplot2, and tidyverse to visualize QC data with filters for date range, assay type, QC product, analyzer, and QC lot number. The data is obtained from an automated download containing all QC data in the LIS over a 24 hour period with a sample identifier, test name, verified date, analyzer result, expected mean and standard deviation, QC product as well as the QC lot number. The data is saved to a shared network drive with access restricted to regional supervisory and technical staff.

The R script is set to automatically import the previous 30 days of QC data and displays the running mean and standard deviation for each test using the applied filters. Using shinydashboard format, more or less data can be viewed by importing more data files or by applying date range filters. Optional filters include test name, analyzer name/type, and QC material, which allow users to assess assay performance down to the instrument level. Multiple tabs are provided to display data in tabular or graphical format, with options for data to be summarize by day, week, or month.

Results: This dashboard provides a method for streamlining QC data review from various analyzer types and vendors, across sites and lot numbers, all of which can be viewed remotely. This provides technical staff the opportunity to quickly get through monthly QC review, as well as identify analyzers which may be seeing shifts in QC running means between analyzers or lot numbers. Ready access to this data allows staff to get through routine QC review quickly, while also promoting better region-wide lab quality and inter-site continuity. Adaptation of this dashboard could also allow review of other quality metrics such as patient running means or hemolysis rates, provided this data is captured in the LIS and access to the raw data is available.

Conclusion: This is a simple, customizable tool that is able to compile QC data for review without the need for investment in expensive or complicated middleware products.

Example1 - Simple.R
Example2 - Reactive selectInput.R
Final_App.R

Supplemental Data Files
.Rhistory
APR1319QCData.csv
APR2026QCData.csv
APR612QCData.csv
R Script to Generate Data for Presentation.R